Stream: Complex Pipelines

Java Streams, Lambda & Functional ProgrammingTopic 23 of 308 questions

Q1Audit each element flowing through a pipeline using peek without changing the data

`peek` lets you watch each element go by for logging or auditing without modifying it; it is a side-effect window, not a transformer.

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Q2From a List<Company>, extract all employees, filter earners > 60000, group by city, sort each group by name

`flatMap` the nested hierarchy, `filter` by salary, then `groupingBy(city, mapping(name, sorted-collector))` produces city-to-sorted-names.

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Q3Merge two lists of employees, remove duplicates by id, group by department, compute average salary

`concat` the two lists, deduplicate by id using `toMap` with a merge function, then `groupingBy(dept, averagingDouble(salary))` for per-department averages.

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Q4Build a pipeline that filters active employees, maps to a DTO, groups by department, sorts each group by salary descending, collects into a LinkedHashMap sorted by department

`filter(active).map(toDto).collect(groupingBy(dept, TreeMap::new, collectingAndThen(toList, sortDesc)))` builds a fully-ordered reporting view in one pipeline.

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Q5Given a List<Company> → List<Department> → List<Employee>, extract all employee names across all companies

Chain one `flatMap` per level of nesting, then `map` to the field you want.

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Q6Why should stream pipelines be side-effect-free, and what breaks if you mutate external state from map()/filter()?

Streams may reorder, skip, or parallelize, so mutating shared state from `map`/`filter` breaks correctness in parallel and causes non-determinism even sequentially.

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Q7Why should you avoid calling stream() inside another stream pipeline (nested streams)?

Nested `stream()` calls signal a missing `flatMap` or a missing precomputed lookup, and often hide O(n*m) cost.

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Q8Use peek() to log each employee as the stream pipeline processes them (debugging only)

`peek` lets you watch each element go by without changing it: a debugging window into a stream pipeline.

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